Elastic Net Regularization Paths for All Generalized Linear Models
| Autor: | Tay, J. Kenneth, Narasimhan, Balasubramanian, Hastie, Trevor |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Statistics::Theory generalized linear models elastic net coordinate descent survival Statistics - Computation Article Statistics::Computation Methodology (stat.ME) Statistics::Machine Learning l1 penalty Cox model Statistics::Methodology regularization path Statistics Probability and Uncertainty lasso Computation (stat.CO) Software Statistics - Methodology |
| Zdroj: | J Stat Softw Journal of Statistical Software; Vol. 106 (2023); 1-31 |
| ISSN: | 1548-7660 |
| Popis: | The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least squares regression, logistic regression and multinomial logistic regression, while Simon, Friedman, Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data. We further extend the reach of the elastic net-regularized regression to all generalized linear model families, Cox models with (start, stop] data and strata, and a simplified version of the relaxed lasso. We also discuss convenient utility functions for measuring the performance of these fitted models. |
| Databáze: | OpenAIRE |
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